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Implicitly abusive comparisons – a new dataset and linguistic analysis

  • We examine the task of detecting implicitly abusive comparisons (e.g. “Your hair looks like you have been electrocuted”). Implicitly abusive comparisons are abusive comparisons in which abusive words (e.g. “dumbass” or “scum”) are absent. We detail the process of creating a novel dataset for this task via crowdsourcing that includes several measures to obtain a sufficiently representative and unbiased set of comparisons. We also present classification experiments that include a range of linguistic features that help us better understand the mechanisms underlying abusive comparisons.

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Metadaten
Author:Michael WiegandORCiDGND, Maja Geulig, Josef RuppenhoferGND
URN:urn:nbn:de:bsz:mh39-104170
URL:https://www.aclweb.org/anthology/2021.eacl-main.27
Parent Title (English):Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Publisher:Association for Computational Linguistics
Place of publication:Stroudsburg, Pennsylvania
Editor:Paola Merlo, Jörg Tiedemann, Reut Tsarfaty
Document Type:Conference Proceeding
Language:English
Year of first Publication:2021
Date of Publication (online):2021/04/22
Publicationstate:Veröffentlichungsversion
Reviewstate:Peer-Review
Tag:abusive comparisons; abusive language; implicitly abusive comparisons
GND Keyword:Beleidigung; Beschimpfung; Crowdsourcing; Datensatz; Vergleich <Rhetorik>
First Page:358
Last Page:368
DDC classes:400 Sprache / 400 Sprache, Linguistik
Open Access?:ja
Leibniz-Classification:Sprache, Linguistik
Linguistics-Classification:Computerlinguistik
Program areas:P2: Mündliche Korpora
Program areas:S2: Forschungskoordination und –infrastrukturen
Licence (English):License LogoCreative Commons - Attribution 4.0 International